In silico designing and immunoinformatics analysis of a novel peptide vaccine against metallo-beta-lactamase (VIM and IMP) variants

The rapid spread of acquired metallo-beta-lactamases (MBLs) among gram negative pathogens is becoming a global concern. Improper use of broad-spectrum antibiotics can trigger the colonization and spread of resistant strains which lead to increased mortality and significant economic loss. In the present study, diverse immunoinformatic approaches are applied to design a potential epitope-based vaccine against VIM and IMP MBLs. The amino acid sequences of VIM and IMP variants were retrieved from the GenBank database. ABCpred and BCPred online Web servers were used to analyze linear B cell epitopes, while IEDB was used to determine the dominant T cell epitopes. Sequence validation, allergenicity, toxicity and physiochemical analysis were performed using web servers. Seven sequences were identified for linear B cell dominant epitopes and 4 sequences were considered as dominant CD4+ T cell epitopes, and the predicted epitopes were joined by KK and GPGPG linkers. Stabilized multi-epitope protein structure was obtained using molecular dynamics simulation. Molecular docking showed that the designed vaccine exhibited sustainable and strong binding interactions with Toll-like receptor 4 (TLR4). Finally, codon adaptation and in silico cloning studies were performed to design an effective vaccine production strategy. Immune simulation significantly provided high levels of immunoglobulins, T helper cells, T-cytotoxic cells and INF-γ. Even though the introduced vaccine candidate demonstrates a very potent immunogenic potential, but wet-lab validation is required to further assessment of the effectiveness of this proposed vaccine candidate.

[1]  Md Sorwer Alam Parvez,et al.  Immunoinformatic Design of a Multivalent Peptide Vaccine Against Mucormycosis: Targeting FTR1 Protein of Major Causative Fungi , 2022, Frontiers in Immunology.

[2]  H. Al Tbeishat Novel In Silico mRNA vaccine design exploiting proteins of M. tuberculosis that modulates host immune responses by inducing epigenetic modifications , 2022, Scientific Reports.

[3]  P. Pongchaikul,et al.  Comprehensive Analysis of Imipenemase (IMP)-Type Metallo-β-Lactamase: A Global Distribution Threatening Asia , 2022, Antibiotics.

[4]  Jale Moradi,et al.  In silico vaccine design and epitope mapping of New Delhi metallo-beta-lactamase (NDM): an immunoinformatics approach , 2021, BMC Bioinformatics.

[5]  Matin Fathollahi,et al.  In silico vaccine design and epitope mapping of New Delhi metallo-beta-lactamase (NDM): an immunoinformatics approach , 2021, BMC Bioinformatics.

[6]  P. Marcatili,et al.  T Cell Epitope Prediction and Its Application to Immunotherapy , 2021, Frontiers in Immunology.

[7]  R. Bonomo,et al.  Specific Protein-Membrane Interactions Promote Packaging of Metallo-β-Lactamases into Outer Membrane Vesicles , 2021, Antimicrobial agents and chemotherapy.

[8]  Susithra Priyadarshni Mugunthan,et al.  Multi-epitope-Based Vaccine Designed by Targeting Cytoadherence Proteins of Mycoplasma gallisepticum , 2021, ACS omega.

[9]  V. Timofeev,et al.  Immunoinformatics analysis to design novel epitope based vaccine candidate targeting the glycoprotein and nucleoprotein of Lassa mammarenavirus (LASMV) using strains from Nigeria , 2021, Journal of biomolecular structure & dynamics.

[10]  W. Kong,et al.  In silico analysis of epitope-based vaccine candidate against tuberculosis using reverse vaccinology , 2021, Scientific reports.

[11]  Asad Ullah,et al.  Immunoinformatics-guided designing and in silico analysis of epitope-based polyvalent vaccines against multiple strains of human coronavirus (HCoV) , 2021, Expert review of vaccines.

[12]  F. Zouein,et al.  Macrophage responses associated with COVID-19: A pharmacological perspective , 2020, European Journal of Pharmacology.

[13]  Fuxun Yu,et al.  Contriving Multi-Epitope Subunit of Vaccine for COVID-19: Immunoinformatics Approaches , 2020, Frontiers in Immunology.

[14]  Shahin Nazarian,et al.  An in silico deep learning approach to multi-epitope vaccine design: a SARS-CoV-2 case study , 2020, Scientific Reports.

[15]  D. Livermore,et al.  Metallo-β-Lactamases: Structure, Function, Epidemiology, Treatment Options, and the Development Pipeline , 2020, Antimicrobial Agents and Chemotherapy.

[16]  Jafar Razmara,et al.  Epitope-based vaccine design: a comprehensive overview of bioinformatics approaches. , 2020, Drug discovery today.

[17]  Jacob A Bauer,et al.  Normal Mode Analysis as a Routine Part of a Structural Investigation , 2019, Molecules.

[18]  R. Bonomo,et al.  Protein determinants of dissemination and host specificity of metallo-β-lactamases , 2019, Nature Communications.

[19]  K. Bush,et al.  Interplay between β-lactamases and new β-lactamase inhibitors , 2019, Nature Reviews Microbiology.

[20]  Daniel W. A. Buchan,et al.  The PSIPRED Protein Analysis Workbench: 20 years on , 2019, Nucleic Acids Res..

[21]  M. U. Mirza,et al.  Antigenic Peptide Prediction From E6 and E7 Oncoproteins of HPV Types 16 and 18 for Therapeutic Vaccine Design Using Immunoinformatics and MD Simulation Analysis , 2018, Front. Immunol..

[22]  W. Elhag,et al.  Prevalence of metallo-β-lactamase acquired genes among carbapenems susceptible and resistant Gram-negative clinical isolates using multiplex PCR, Khartoum hospitals, Khartoum Sudan , 2018, BMC Infectious Diseases.

[23]  H. Wako,et al.  Normal mode analysis as a method to derive protein dynamics information from the Protein Data Bank , 2017, Biophysical Reviews.

[24]  V. Prajapati,et al.  Exploring Leishmania secretory proteins to design B and T cell multi-epitope subunit vaccine using immunoinformatics approach , 2017, Scientific Reports.

[25]  V. Prajapati,et al.  Exploring Leishmania secretory proteins to design B and T cell multi-epitope subunit vaccine using immunoinformatics approach , 2017, Scientific Reports.

[26]  Robin Curtis,et al.  Protein–Sol: a web tool for predicting protein solubility from sequence , 2017, Bioinform..

[27]  M. Adams,et al.  Global Molecular Epidemiology of IMP-Producing Enterobacteriaceae , 2017, Antimicrobial Agents and Chemotherapy.

[28]  Pascal Retailleau,et al.  Beta-lactamase database (BLDB) – structure and function , 2017, Journal of enzyme inhibition and medicinal chemistry.

[29]  A. Komar,et al.  The importance of mRNA structure in determining the pathogenicity of synonymous and non‐synonymous mutations in haemophilia , 2017, Haemophilia : the official journal of the World Federation of Hemophilia.

[30]  Y. Ghasemi,et al.  A panoramic review and in silico analysis of IL-11 structure and function. , 2016, Cytokine & growth factor reviews.

[31]  R. Bonomo,et al.  Membrane-anchoring stabilizes and favors secretion of New Delhi Metallo-β-lactamase , 2016, Nature chemical biology.

[32]  Bostjan Kobe,et al.  Recombinant and epitope-based vaccines on the road to the market and implications for vaccine design and production , 2016, Human vaccines & immunotherapeutics.

[33]  R. Norton,et al.  Strain-transcending immune response generated by chimeras of the malaria vaccine candidate merozoite surface protein 2 , 2016, Scientific Reports.

[34]  C. Schofield,et al.  Comparison of Verona Integron-Borne Metallo-β-Lactamase (VIM) Variants Reveals Differences in Stability and Inhibition Profiles , 2015, Antimicrobial Agents and Chemotherapy.

[35]  A. Vila,et al.  Overcoming differences: The catalytic mechanism of metallo‐β‐lactamases , 2015, FEBS letters.

[36]  Berk Hess,et al.  GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers , 2015 .

[37]  S. C. T. P. Rts Efficacy and safety of RTS,S/AS01 malaria vaccine with or without a booster dose in infants and children in Africa: final results of a phase 3, individually randomised, controlled trial , 2015, The Lancet.

[38]  Erin E. Gill,et al.  Antibiotic Adjuvants: Diverse Strategies for Controlling Drug-Resistant Pathogens , 2014, Chemical biology & drug design.

[39]  J. Rolain,et al.  Carbapenemase genes and genetic platforms in Gram-negative bacilli: Enterobacteriaceae, Pseudomonas and Acinetobacter species. , 2014, Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases.

[40]  A. Vila,et al.  Evolution of Metallo-β-lactamases: Trends Revealed by Natural Diversity and in vitro Evolution , 2014, Antibiotics.

[41]  Darren R. Flower,et al.  AllerTOP v.2—a server for in silico prediction of allergens , 2014, Journal of Molecular Modeling.

[42]  Enrique S. Quintana-Ortí,et al.  iMODS: internal coordinates normal mode analysis server , 2014, Nucleic Acids Res..

[43]  Zhenglun Liang,et al.  Development of enterovirus 71 vaccines: from the lab bench to Phase III clinical trials , 2014, Expert review of vaccines.

[44]  Fattma A. Ali,et al.  DETECTION OF METALLO Β-LACTAMASE ENZYME IN SOME GRAM NEGATIVE BACTERIA ISOLATED FROM BURN PATIENTS IN SULAIMANI CITY, IRAQ , 2014 .

[45]  I. Vorobyov,et al.  The different interactions of lysine and arginine side chains with lipid membranes. , 2013, The journal of physical chemistry. B.

[46]  Rahul Kumar,et al.  In Silico Approach for Predicting Toxicity of Peptides and Proteins , 2013, PloS one.

[47]  Chaok Seok,et al.  GalaxyRefine: protein structure refinement driven by side-chain repacking , 2013, Nucleic Acids Res..

[48]  Irini A. Doytchinova,et al.  AllerTOP - a server for in silico prediction of allergens , 2013, BMC Bioinformatics.

[49]  Abdelmonaem Messaoudi,et al.  Homology modeling and virtual screening approaches to identify potent inhibitors of VEB-1 β-lactamase , 2013, Theoretical Biology and Medical Modelling.

[50]  Christian Melander,et al.  Overcoming resistance to β-lactam antibiotics. , 2013, The Journal of organic chemistry.

[51]  Jian Peng,et al.  Template-based protein structure modeling using the RaptorX web server , 2012, Nature Protocols.

[52]  P. Stadler,et al.  ViennaRNA Package 2.0 , 2011, Algorithms for Molecular Biology : AMB.

[53]  Thierry Naas,et al.  Global Spread of Carbapenemase-producing Enterobacteriaceae , 2011, Emerging infectious diseases.

[54]  N. Strynadka,et al.  Crystal structure of New Delhi metallo‐β‐lactamase reveals molecular basis for antibiotic resistance , 2011, Protein science : a publication of the Protein Society.

[55]  N. Soares,et al.  Horizontal Transfer of the OXA-24 Carbapenemase Gene via Outer Membrane Vesicles: a New Mechanism of Dissemination of Carbapenem Resistance Genes in Acinetobacter baumannii , 2011, Antimicrobial Agents and Chemotherapy.

[56]  Morten Nielsen,et al.  Peptide binding predictions for HLA DR, DP and DQ molecules , 2010, BMC Bioinformatics.

[57]  Massimo Bernaschi,et al.  Computational Immunology Meets Bioinformatics: The Use of Prediction Tools for Molecular Binding in the Simulation of the Immune System , 2010, PloS one.

[58]  V. Miriagou,et al.  Acquired carbapenemases in Gram-negative bacterial pathogens: detection and surveillance issues. , 2010, Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases.

[59]  G. Jacoby,et al.  Updated Functional Classification of β-Lactamases , 2009, Antimicrobial Agents and Chemotherapy.

[60]  M. Kessel,et al.  E. coli transports aggregated proteins to the poles by a specific and energy-dependent process. , 2009, Journal of molecular biology.

[61]  H. Maltezou Metallo-beta-lactamases in Gram-negative bacteria: introducing the era of pan-resistance? , 2009, International journal of antimicrobial agents.

[62]  William Martin,et al.  Epitope-Based Immunome-Derived Vaccines: A Strategy for Improved Design and Safety , 2008, Clinical Applications of Immunomics.

[63]  M. Page,et al.  The Mechanisms of Catalysis by Metallo β-Lactamases , 2008, Bioinorganic chemistry and applications.

[64]  Vasant G Honavar,et al.  Predicting linear B‐cell epitopes using string kernels , 2008, Journal of molecular recognition : JMR.

[65]  Ruth Nussinov,et al.  FireDock: Fast interaction refinement in molecular docking , 2007, Proteins.

[66]  Manfred J. Sippl,et al.  Thirty years of environmental health research--and growing. , 1996, Nucleic Acids Res..

[67]  Irini A. Doytchinova,et al.  BMC Bioinformatics BioMed Central Methodology article VaxiJen: a server for prediction of protective antigens, tumour , 2007 .

[68]  C. Parra-López,et al.  A Linear Peptide Containing Minimal T- and B-Cell Epitopes of Plasmodium falciparum Circumsporozoite Protein Elicits Protection against Transgenic Sporozoite Challenge , 2006, Infection and Immunity.

[69]  Sudipto Saha,et al.  Prediction of continuous B‐cell epitopes in an antigen using recurrent neural network , 2006, Proteins.

[70]  G. Ya. Wiederschain,et al.  The proteomics protocols handbook , 2006, Biochemistry (Moscow).

[71]  John Sidney,et al.  Predicting population coverage of T-cell epitope-based diagnostics and vaccines , 2006, BMC Bioinformatics.

[72]  B. Hall,et al.  Structure-Based Phylogeny of the Metallo-β-Lactamases , 2005, Antimicrobial Agents and Chemotherapy.

[73]  Dieter Jahn,et al.  JCat: a novel tool to adapt codon usage of a target gene to its potential expression host , 2005, Nucleic Acids Res..

[74]  Ruth Nussinov,et al.  PatchDock and SymmDock: servers for rigid and symmetric docking , 2005, Nucleic Acids Res..

[75]  Timothy R. Walsh,et al.  Metallo-β-Lactamases: the Quiet before the Storm? , 2005, Clinical Microbiology Reviews.

[76]  J. Nyhus,et al.  HLA- and dose-dependent immunogenicity of a peptide-based HIV-1 immunotherapy candidate (Vacc-4x) , 2004, AIDS.

[77]  Ian W. Davis,et al.  Structure validation by Cα geometry: ϕ,ψ and Cβ deviation , 2003, Proteins.

[78]  N. Bagge,et al.  Antibodies against β‐lactamase can improve ceftazidime treatment of lung infection with β‐lactam‐resistant Pseudomonas aeruginosa in a rat model of chronic lung infection , 2002, APMIS : acta pathologica, microbiologica, et immunologica Scandinavica.

[79]  P. Nordmann,et al.  Emerging carbapenemases in Gram-negative aerobes. , 2002, Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases.

[80]  B. F. Hall,et al.  Synthetic malaria peptide vaccine elicits high levels of antibodies in vaccinees of defined HLA genotypes. , 2000, The Journal of infectious diseases.

[81]  R. Hodges,et al.  Immunization with a Pseudomonas aeruginosa elastase peptide reduces severity of experimental lung infections due to P. aeruginosa Or Burkholderia cepacia. , 2000, The Journal of infectious diseases.

[82]  Liam J. McGuffin,et al.  The PSIPRED protein structure prediction server , 2000, Bioinform..

[83]  T. Yeates,et al.  Verification of protein structures: Patterns of nonbonded atomic interactions , 1993, Protein science : a publication of the Protein Society.

[84]  Serge Pérez,et al.  Carbohydrate-protein interactions: molecular modeling insights. , 2014, Advances in carbohydrate chemistry and biochemistry.

[85]  John M. Walker,et al.  The Proteomics Protocols Handbook , 2005, Humana Press.

[86]  P. Nordmann,et al.  Metallo-beta-lactamases: the quiet before the storm? , 2005, Clinical microbiology reviews.

[87]  N. Høiby,et al.  Chromosomal beta-lactamase is packaged into membrane vesicles and secreted from Pseudomonas aeruginosa. , 2000, The Journal of antimicrobial chemotherapy.

[88]  R D Appel,et al.  Protein identification and analysis tools in the ExPASy server. , 1999, Methods in molecular biology.